35 results about "Local planning" patented technology

The invention discloses a complex environment-oriented body agent navigation and three-dimensional reconstruction method, and relates to the technical field of body agent navigation, and the method comprises the following steps: collecting environment data around a navigation body, using a fine-grained octree for close-range key areas (narrow slits and dead corners), adopting coarse voxel acceleration for a far area, and adopting coarse voxel acceleration for a near-range key area (narrow slits and dead corners); extracting large planes such as wall surfaces, beam columns and the like by using RANSAC (Random Sample Consensus), and constructing an environment'abstract skeleton 'for navigation reference; establishing a temporary three-dimensional space based on the point cloud of the whole body environment, dividing a plurality of detection planes in the space, performing position correction on an obstacle edge dot matrix in the detection planes, determining a movable range, and generating a safe motion path in a feasible space by applying a local planning algorithm; octree + differential coding is carried out on a local map, and only a current activity area and crisis point cloud are reserved; local full-stack deployment enables navigation to be free of dependence on remote communication, and sensing, mapping and navigation closed loop can still be completed when a link is broken.

The invention discloses a robot path planning method and device, equipment, a medium and a product, and relates to the field of robot obstacle avoidance, and the method comprises the steps: constructing a grid map of a target environment; according to the grid map, adopting an improved A * algorithm to obtain a global planning path; the improved A * algorithm constructs a dynamic heuristic function based on geometric distance and dynamic risk parameters on the basis of the A * algorithm; according to the global planning path, adopting a path decomposition method and an improved dynamic window algorithm to obtain a plurality of local planning paths; the improved dynamic window algorithm constructs an evaluation function based on a cross-modal fusion method and a collision time method on the basis of a dynamic window algorithm; and obtaining a robot path planning result according to all the local planning paths. According to the invention, the global path and local path planning of the robot is realized based on the improved A * algorithm and the improved dynamic window algorithm, the path planning efficiency and safety are improved, and the obstacle avoidance capability of the robot in a complex environment is enhanced.

The invention provides an unmanned aerial vehicle autonomous exploration method based on boundary detection and probability sampling fusion, and the method comprises the steps: firstly generating a high-quality viewpoint through a probability sampling model, and providing precise data support for path planning in an exploration stage; secondly, efficient collaboration of global planning and local planning is achieved by means of a layered planning architecture and a topological path search technology; and finally, through a re-planning mechanism, the robustness and task continuity of the unmanned aerial vehicle in the exploration process in a complex unknown environment are ensured. Aiming at the technical defects that the existing autonomous exploration method is easy to fall into local optimum, low in calculation efficiency, insufficient in track smoothness and the like, the invention provides a solution suitable for an unknown environment, and a space exploration task can be efficiently and quickly completed.

The invention relates to a self-balancing bicycle robot multi-task path planning method, which belongs to the technical field of mobile robot autonomous navigation, and comprises the following steps: constructing an environment grid model containing static obstacle information, and fusing obstacle repulsive force and a Voronoi diagram skeleton to guide and generate an environment safety potential field; receiving a multi-task target point sequence, and calculating an optimal cut-in course angle of each target point; performing global path search in a three-dimensional state space by adopting a forward-backward bimodal primitive to generate a global path meeting incomplete kinematics constraints; the local planning adopts an improved dynamic window algorithm, dynamic stability verification and pruning are carried out in the space of the linear speed and the front wheel steering angle, and an optimal instruction is selected. The technical problems of discontinuous path, dynamic instability, poor obstacle avoidance safety and the like during multi-task operation of the self-balancing bicycle robot are solved, and the operation safety, smoothness and efficiency of the robot in a complex unstructured environment are effectively and remarkably improved.

The invention discloses a multi-agent priority obstacle avoidance path planning method for a dynamic obstacle, and relates to the technical field of mobile agent path planning, the method comprises the following steps: a global planning stage: optimizing an A * algorithm by introducing a heuristic scaling factor, an obstacle and direction penalty term and a neighborhood adaptive extension mechanism, a path is smoothed by combining broken line reconstruction and a Bezier curve; in the local planning stage, the weight of a DWA evaluation function is dynamically adjusted through fuzzy control, and the real-time obstacle avoidance robustness is improved. Meanwhile, a priority obstacle avoidance strategy and a dynamic look-ahead mechanism are designed, multi-agent conflicts are coordinated, and collective stagnation is avoided. Simulation results show that the method shows better path length, search efficiency and obstacle avoidance performance in static, unknown static and dynamic obstacle environments, effectively improves the multi-agent coordination stability, and is suitable for actual scenes such as warehouse logistics and the like.

The invention belongs to the technical field of robots, and particularly relates to a robot local planning method and device, a computer readable storage medium and a robot. The method comprises the following steps: acquiring a local occupation grid map of the robot; generating point cloud data of the robot according to the local occupation grid map; wherein the point cloud data is an intersection point set of each ray taking the central position of the robot as a ray origin and an obstacle area in the locally occupied grid map; performing reinforcement learning training on a preset local planning network model based on the point cloud data to obtain a trained local planning network model; and performing local planning on the robot by using the trained local planning network model to obtain a local planning result of the robot. According to the method and the device, the local occupation grid map can be simplified and converted into the point cloud data, so that the point cloud data can be applied to reinforcement learning of local planning of the robot, and the performance of the local planning of the robot can be improved.

The invention provides a hybrid local planning method and system based on a dynamic state diagram, and the method comprises the steps: calculating a displacement distance range and an orientation angle range of a target point based on the operation parameters, obtained in real time, of an autonomous mobile robot and a pre-constructed initial dynamic diagram; calculating state nodes based on the target point displacement distance range, the orientation angle range and the initial dynamic graph, and constructing a dynamic state graph through the state nodes; constructing a target path based on the dynamic state diagram and a preset search algorithm to obtain a target point, and obtaining a target linear speed and a target point orientation according to the target point and a preset speed strategy; obtaining a target angular velocity based on the target point and a preset path planning algorithm; and sending the target linear velocity, the target angular velocity and the target point orientation to the autonomous mobile robot, so that the autonomous mobile robot plans a local path. According to the method, the optimal path is dynamically and locally adjusted in real time under kinematics constraints, and the path planning reliability is improved.

In conventional robot navigation techniques learning and planning algorithms act independently without guiding each other simultaneously. A method and system for robotic navigation with simultaneous local path planning and learning is disclosed. The method discloses an approach to learn and plan simultaneously by assisting each other and improve the overall system performance. The planner acts as an actuator and helps to balance exploration and exploitation in the learning algorithm. The synergy between dynamic window approach (DWA) as a planning algorithm and a disclosed Next best Q-learning (NBQ) as a learning algorithm offers an efficient local planning algorithm. Unlike the traditional Q-learning, dimension of Q-tree in the NBQ is dynamic and does not require to define a priori.

The invention discloses a material carrying control method and device, a medium and a product, and relates to the technical field of multi-agent collaboration.The method comprises the steps that a global shortest path is determined through a double-layer FBS model self-closed-loop architecture; carrying materials from an initial position according to a global shortest path, and carrying out planned adjustment in real time in the carrying process to complete a global task target; wherein the planning adjustment process of the rescue material at any current position comprises the following steps: acquiring a local task target at the current position; determining a target planning algorithm at the current position based on the local task target at the current position by using a double-layer FBS model self-closed loop architecture; determining a local planning behavior at the current position according to a target planning algorithm at the current position by using a double-layer FBS model self-closed loop architecture; and carrying the relief goods at the current position based on the local planning behavior at the current position. According to the invention, material carrying control in a complex rescue scene can be realized.

The invention relates to an unmanned sweeper covering type sweeping operation method and system and a medium, and the method comprises the steps: M1, collecting the boundary of a to-be-swept area through a handheld positioning data collection device, importing the boundary into a cloud end, and enabling the precision of positioning data to reach the centimeter level; m2, a cloud global planning algorithm module reads vehicle end parameters and plans a coverage type global path through an algorithm in combination with region boundary information; and M3, the vehicle end local planning algorithm module receives the global path, intercepts the global path as a local planning path according to the current positioning, carries out longitudinal planning, generates a complete planning track, and issues the complete planning track to the control module. According to the method, the problems that an unmanned sweeping vehicle is difficult to realize coverage type sweeping operation and cannot adapt to a complex and changeable urban environment, and a traditional coverage type planning algorithm is long in time consumption are solved, and mutually restricted optimization dimensions such as obstacle avoidance, sweeping blind area elimination and operation efficiency improvement can be processed at the same time.

This invention discloses a collaborative path planning method based on multi-constraint improved A* algorithm and a dynamic look-ahead mechanism. It constructs a feature distance field map containing a nonlinear safety potential field, transforming discrete environmental information into continuous safety gradients. An improved A* algorithm, incorporating dynamic adaptive heuristic weights, nonlinear safety potential field costs, and kinematic smoothness constraints, is used to plan the globally optimal path. A velocity-adaptive dynamic look-ahead mechanism is established to calculate dynamic look-ahead points that conform to the current motion state in real time. A multi-objective evaluation function containing a global traction index is introduced into the local planning process to optimize control commands by measuring the deviation between the local trajectory and the global intent. This invention effectively solves the problems of "wall-hugging effect," high computational cost in large-scale environments, and poor coordination between global and local planning in traditional methods, improving the navigation safety and smoothness of robots in complex unstructured scenarios.

This invention relates to a multi-task path planning method for a self-balancing bicycle robot, belonging to the field of autonomous navigation technology for mobile robots. The method includes: constructing an environmental grid model containing static obstacle information; fusing obstacle repulsion forces and a Venn diagram skeleton to guide the generation of a safe environmental potential field; receiving a sequence of multi-task target points and calculating the optimal heading angle for each target point; performing a global path search in a three-dimensional state space using forward-backward dual-modal primitives to generate a global path that satisfies nonholonomic kinematic constraints; and employing an improved dynamic window algorithm for local planning, performing dynamic stability verification and pruning within the space of linear velocity and front wheel steering angle to select the optimal instruction. This invention solves the technical problems of discontinuous paths, dynamic instability, and poor obstacle avoidance safety in multi-task operations of self-balancing bicycle robots, effectively and significantly improving the safety, smoothness, and efficiency of robot operations in complex unstructured environments.

Systems and methods for autonomous-vehicle navigation integrating path planning with a perception network. A Bird's Eye View costmap is generated at runtime using only onboard sensors. No external localization providers are used.

The invention discloses a cleaning robot path planning method based on adaptive partitioning and reinforcement learning, and belongs to the technical field of robots. According to the method, a double-layer planning framework is adopted, in a global planning layer, an adaptive tree partitioning algorithm (ATPA) is provided, working areas are dynamically divided according to environment layout, waste material distribution and robot load capacity, entrances and exits of all the areas are optimized, and macroscopic guidance is provided for local planning; in a local planning layer, an improved reinforcement learning algorithm based on a dominant actor-commentator (A2C) is provided, and a robot is guided to generate an ordered and smooth refined coverage path by designing a dual reward mechanism of'departing from a starting point 'and'linear motion'. According to the method, through the synergistic effect of global partition optimization and local strategy learning, the path length and the turning frequency are remarkably reduced, the path redundancy rate is minimized, multi-target collaborative optimization is achieved, and the overall efficiency and the intelligent level of cleaning operation are improved.

The application discloses a kind of intelligent vehicle local path planning method, device and storage medium, wherein method includes: according to positioning information and high-precision map information, the reference driving path required for intelligent car local path planning is obtained;According to the planning starting point of current planning frame is solved according to tracking effect;Smooth the reference driving path, obtain path frenet coordinate system according to the reference driving path after smoothing;Static obstacles and oncoming vehicles are projected to path frenet coordinate system, SL graph is generated, and path planning trajectory is obtained according to SL graph;Dynamic obstacle short-time prediction trajectory is projected to speed frenet coordinate system, ST graph is generated, and speed planning trajectory is obtained according to ST graph;Path planning trajectory and speed planning trajectory are coupled on natural coordinate system, and restore into high-dimensional local planning trajectory.The application uses SL graph and ST graph to process path and speed respectively, improves efficiency, and can be widely applied to trajectory planning technical field in unmanned driving.

This application provides a method and system for generating obstacle avoidance trajectories based on a diffusion strategy, relating to the field of intelligent control technology. The method acquires the operating status information, task target information, and environmental status information of a mobile robot; determines the local planning area requiring local obstacle avoidance planning; identifies obstacle occupancy information within the local planning area; determines the location and local deformation of facility boundaries within the local planning area; and determines the effective passage boundary. Starting from multiple initial noise sequences, it performs multi-step inverse diffusion denoising iterations under the constraints of guiding condition features to generate multiple candidate obstacle avoidance trajectories. Candidate obstacle avoidance trajectories that conflict with obstacle occupancy information, exceed the effective passage boundary, or do not meet execution constraints are eliminated, and the target obstacle avoidance trajectory is determined from the remaining candidate trajectories. This method can improve the safety and passage efficiency of local obstacle avoidance trajectories in operating environments where the boundaries of facilities on both sides of the passage may deform.

Embodiments of the present application provide a path planning method, device, automatic gardening equipment and computer program product. The path planning method comprises: receiving a preset walking direction of a working area; dividing the working area into a plurality of sub-areas; for each sub-area, determining an actual planning direction of the sub-area from the preset walking direction and a recommended planning direction of the sub-area, and determining a local planning path corresponding to the sub-area according to the actual planning direction, wherein the path length of the sub-area traversed along the recommended planning direction is the shortest; obtaining a moving sequence between the sub-areas; and determining a global planning path of the working area according to the local planning path of each sub-area and the moving sequence between the sub-areas. The method is more effective.

The invention belongs to the technical field of mobile robot path planning, and provides a global-local path planning method adaptive to fruit tree inter-row operation. According to the method, a dynamic weight mechanism is introduced into an A * algorithm in global path planning, and the weight of a heuristic function is adaptively adjusted according to two factors, namely the ratio of the distance from a node to a target point and the transverse spacing of fruit trees, so that the search precision is improved; a * algorithm is utilized to pre-plan and construct a real cost matrix which is used for guiding segmented optimization of the ant colony algorithm, meanwhile, an adaptive pheromone volatilization mechanism based on a solution improvement rate is adopted to improve convergence stability, and path smoothing is realized in combination with redundant point deletion. In local planning, a path deviation cost item is introduced into a dynamic window method to quantify trajectory deviation, and a dynamic weighting strategy is adopted to adaptively adjust the weight of each evaluation function according to the obstacle distance. According to the method, efficient, smooth and stable path planning can be realized in a complex orchard environment, and the method is suitable for a mobile robot for fruit tree row-to-row operation.

The invention provides a space-time tensor task priority local planning method for an unmanned surface vessel, and belongs to the technical field of autonomous unmanned system navigation and control, and the method comprises the steps: constructing a unified three-dimensional space-time tensor based on environment perception data; the three-dimensional space-time tensor performs integrated coding on the static obstacle information and the future prediction occupation information of the dynamic obstacle, and supports collision feasibility query of constant time complexity of any position and time combination; generating guidance reference information containing an expected course based on the time parameterization reference trajectory; generating a candidate control action set based on the current motion state, carrying out collision constraint verification on candidate control actions based on the three-dimensional space-time tensor, screening to obtain a safety action set meeting the requirement of no collision constraint, and taking the deviation between the minimum predicted ship course and the expected course as a unique optimization target in the safety action set. Selecting an optimal control action; and 4, generating a local planning path based on the optimal control action, and controlling the unmanned surface vessel to sail.

The invention discloses a multi-modal fusion tidal flat robot adaptive path planning method. The method comprises the following steps: (1) dynamic environment prediction; (2) multi-source data fusion; (3) path planning dynamic constraint adaptation; (4) performing global-local planning connection; according to the method, a data fusion module, an environment modeling module, a self-adaptive planning algorithm module and a path verification and adjustment module are arranged and are used for cooperative work to realize precise navigation of the tidal flat robot. The data fusion module serves as a core data support of the whole planning method and realizes multi-level fusion of ocean mode data, remote sensing inversion data and machine vision data; and the environment modeling module constructs a dynamic environment model based on the fused data, the dynamic environment model is divided into a global environment model and a local environment model, and combination of global overall planning and local fine perception is realized. Through deep fusion of an ocean mode, remote sensing inversion and machine vision, the limitation of traditional single data source path planning is broken through.

The invention provides a method for autonomously exploring in an indoor unknown environment for a mobile robot, the pose of the robot and an environment map are obtained through instant positioning and mapping, and a planning strategy of mutual switching of local exploration planning and global exploration planning is used. The robot generates candidate targets through random sampling in the local planning stage, the gain of the candidate targets is calculated, the optimal gain is obtained to serve as a local planning target point, and when no local boundary exists around the robot, the robot is switched to the global planning stage; in the global planning stage, the optimal global boundary point is used as an exploration target, after the robot reaches the target, the robot is switched into local exploration planning, and a new non-explored local area is searched again; when the robot does not have the global boundary, exploration is completed, and the robot returns to the initial position. Through mutual switching of local planning and global planning, the robot is guided to always explore an unknown area, and finally exploration traversal and mapping of the whole indoor area are completed.

The dammed lake capacity measurement underwater robot adaptive dynamic path planning method comprises the following steps: a shore-based system processes dammed lake images collected by a satellite subscription camera, obtains initial boundary longitude and latitude information of the dammed lake water area, and performs rasterization processing on the dammed lake water area map; after the underwater robot is launched, the shore-based system sends the divided dammed lake area boundary information and the starting position information to each underwater robot through a communication module; the underwater robot executes an initial area coverage path, a sensor carried by the underwater robot acquires real-time underwater robot bottom height information, a local planner adopts an adaptive dynamic form to calculate and obtain a new local path; according to the scanning width and the path length of the local planning path, combined with the average bottom height on the underwater robot motion path; the scanned area is compared with the initial area of the dammed lake to determine whether the underwater robot completes the area coverage scanning task.

The invention discloses a limited space large-scale equipment transportation system and method based on an improved A * algorithm and deep reinforcement learning, and the system comprises an initial planning module, a global path optimization module and a local path dynamic optimization module. The global path optimization module is used for generating a global optimal transport path and planning an initial global transport path of the large-scale equipment based on an A * algorithm, the global path optimization module is used for adjusting weight coefficients of actual cost and heuristic cost in a heuristic function based on the initial global transport path to generate the global optimal transport path, and the local path dynamic optimization module is used for dynamically optimizing the global optimal transport path based on a deep reinforcement learning algorithm. A local planning network of an Actor-Critic architecture is constructed, obstacle information is acquired in real time, postures of a local transportation path and the large equipment are dynamically adjusted based on the obstacle information and sliding window information of a global optimal transportation path, and a collision-free optimal transportation path is generated, so that the transportation efficiency and safety of the large equipment are improved.

The embodiment of the invention provides a robot obstacle avoidance path planning method, device and equipment, a robot and a medium. The method comprises the steps of performing path planning based on a current position and a final position of a robot, and determining a plurality of local path intervals; performing obstacle avoidance planning on each local path interval, and determining a target local planning path corresponding to each local path interval; and splicing the target local planning paths corresponding to the plurality of local path intervals in sequence, and determining a target global planning path from the current position to the final position. According to the method, the global planning path of the target is obtained through local planning, the planning speed of path planning can be increased, the planning overhead can be reduced, and in the local obstacle avoidance planning process, the suddenly appearing obstacle situation can be considered, so that in the process that the robot moves according to the planned global planning path of the target, the obstacle avoidance efficiency of the robot is improved. Obstacles are reasonably avoided, and the obstacle avoidance capability of the robot is improved.

The application provides a valet parking method with local autonomous planning decision function, comprising: acquiring multi-source perception information of a current target vehicle according to at least one information acquisition device arranged around the target vehicle; obtaining complete obstacle information around the current target vehicle based on a multi-source perception data fusion algorithm; constructing a kinematic model and a single-track model of the target vehicle, and calculating an optimal driving local path of the target vehicle when safely passing a road with obstacles according to the obstacle information; and controlling the target vehicle to return to the path for continuous driving. By adopting a combination of a high-precision map global path and a local planning path, the entire process of valet parking is guaranteed to be efficient and reliable, the compatibility of the system is improved, and the application meets the advantages of having the ability of local autonomous planning in a complex scene, being adaptive to a complex scene with lack of coverage network and dim light, and handling a sudden situation of an intruding vehicle.

The invention discloses a power system adjustment resource configuration method based on a multi-agent game, terminal equipment and a computer readable storage medium, and the method comprises the steps: taking new energy power prediction data and power grid load data as input based on a multi-agent game framework, training and generating an adjustment resource planning model, the adjustment resource planning model is used for outputting a capacity and proportion configuration scheme of adjustment resources; based on the adjustment resource planning model, generating corresponding local planning models for different geographic areas through a local distillation mechanism; and performing adjustment resource configuration on the geographic area through the local planning model, and summarizing configuration results to the adjustment resource planning model for global coordination. The problem of unreasonable resource allocation adjustment after high-proportion new energy is accessed to the power system is solved, and dynamic optimal matching of resource allocation adjustment and multi-subject interest appeals is achieved.

The present application relates to village planning space information processing technical field, disclose a kind of village planning real scene three-dimensional data acquisition method, device, equipment and storage medium, by establishing village rough three-dimensional scene and dividing planning observation unit, make data evaluation process by overall coverage determination turn to local planning expression determination, introduce planning interaction record and respectively calculate initial collection completeness, planning importance and required collection completeness, so that system can identify lane facade, courtyard edge, eaves lower interface and public space boundary and other local space area that influence planning expression effect, by calculating reinforcement requirement value and generating supplementary collection record table, so that supplementary collection process is transformed into differentiating reinforcement processing for key planning observation unit, finally by integrating initial oblique image and supplementary collection image, realize local depth estimation and real scene three-dimensional fusion, improve the three-dimensional expression quality of key area such as narrow lane, courtyard boundary and low-texture facade.

Embodiments of the present application disclose a TEB-based path planning method, device and equipment, wherein the method comprises the following steps: determining a planning starting point of a local planning path; selecting a plurality of splicing points; generating a first part of the local planning path according to the planning starting point and the plurality of splicing points; intercepting a segment of a global planning line after a corresponding point of the last splicing point as a reference planning line; generating a second part of the local planning path according to a preset trajectory constraint function and the reference planning line; and splicing the first part and the second part of the local planning path to form the local planning path. Through the present application, the problem of left and right swinging of a robot when encountering an obstacle can be solved, and the smoothness and stability of path planning are improved.

The application provides a spatiotemporal tensor task priority local planning method for unmanned surface vehicle, and belongs to the technical field of autonomous unmanned system navigation and control, and comprises the following steps: constructing a unified three-dimensional spatiotemporal tensor based on environment perception data; the three-dimensional spatiotemporal tensor integrally encodes static obstacle information and future prediction occupation information of dynamic obstacles, supports constant time complexity collision feasibility query of any position and time combination; generating guidance reference information containing expected heading based on time parameterized reference trajectory; generating a candidate control action set based on the current motion state, performing collision constraint verification on the candidate control action based on the three-dimensional spatiotemporal tensor, screening to obtain a safe action set satisfying the non-collision constraint, and selecting an optimal control action in the safe action set with the only optimization target of minimizing the deviation between the predicted ship heading and the expected heading; and step four, generating a local planning path based on the optimal control action, and controlling the unmanned surface vehicle to sail.

The application discloses a robot path planning method, device, equipment, medium and product, and relates to the field of robot obstacle avoidance. The method comprises the following steps: constructing a grid map of a target environment; obtaining a global planning path by using an improved A* algorithm according to the grid map; constructing a dynamic heuristic function based on a geometric distance and a dynamic risk parameter on the basis of the A* algorithm; obtaining a plurality of local planning paths by using a path decomposition method and an improved dynamic window algorithm according to the global planning path; constructing an evaluation function based on a cross-modal fusion method and a collision time method on the basis of the dynamic window algorithm; and obtaining a robot path planning result according to all the local planning paths. The application realizes global path planning and local path planning of a robot based on the improved A* algorithm and the improved dynamic window algorithm, improves path planning efficiency and safety, and enhances the obstacle avoidance capability of the robot in a complex environment.